# How to Build a RAG Pipeline with LangChain and Claude > A step-by-step, copy-pasteable tutorial: load and chunk your docs, embed and index them, retrieve the right context, and ground Claude's answers in your own sources — with a working repo to clone. **Category:** Tutorial **Author:** NeuralSeek Team · **Published:** June 17, 2026 **Canonical:** https://neuralseek.ai/ai-grounded/build-rag-pipeline-langchain-claude **Section index:** https://neuralseek.ai/ai-grounded Retrieval-augmented generation (RAG) is the most reliable way to make a large language model answer from your data instead of its training set — and it's surprisingly approachable to build. This is a hands-on, copy-pasteable tutorial for wiring up a RAG pipeline with LangChain for orchestration and Anthropic's Claude as the answering model. We'll go from raw documents to a grounded, source-backed answer in four steps, and there's a working repo to clone at the end so you can run the whole thing locally. No hand-waving — every step has the code you actually need. ## What you'll build By the end you'll have a small but complete RAG pipeline: a script that ingests a folder of documents, splits them into retrievable chunks, embeds those chunks into a vector index, retrieves the most relevant ones for any question, and asks Claude to answer using only that retrieved context. The same four-stage shape — load and chunk, embed and index, retrieve, ground — underpins every production RAG system, so what you build here scales directly to real workloads. ## Prerequisites You'll need Python 3.10+, an Anthropic API key for Claude, and a couple of packages. Install them with: pip install langchain langchain-anthropic langchain-community chromadb. Set your key with export ANTHROPIC_API_KEY=sk-ant-... and drop a few .txt or .pdf files into a ./docs folder to use as your knowledge base. That's the entire setup. ## Step 1 — Load and chunk your documents LLMs have a finite context window, so you can't just paste whole documents in. Load your files and split them into overlapping chunks so that retrieval can surface the precise passage a question needs: from langchain_community.document_loaders import DirectoryLoader; from langchain.text_splitter import RecursiveCharacterTextSplitter; docs = DirectoryLoader('./docs').load(); chunks = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150).split_documents(docs). The overlap matters — it keeps sentences that straddle a chunk boundary from getting orphaned. ## Step 2 — Embed and index Now turn each chunk into a vector and store it in a searchable index. We'll use Chroma as a local vector store: from langchain_community.vectorstores import Chroma; from langchain_community.embeddings import HuggingFaceEmbeddings; embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2'); vectorstore = Chroma.from_documents(chunks, embeddings, persist_directory='./index'). Persisting the index means you embed once and reuse it on every subsequent run. ## Step 3 — Retrieve the right context At query time, convert the index into a retriever that returns the top-k most relevant chunks for a question: retriever = vectorstore.as_retriever(search_kwargs={'k': 4}); context_docs = retriever.invoke('What is our refund policy?'). Tuning k is the single biggest lever on answer quality — too low and you starve the model of context, too high and you drown it in noise. Four is a sane default to start from. > RAG isn't about a smarter model — it's about putting the right four paragraphs in front of the model at the right moment. ## Step 4 — Ground the answer with Claude Finally, hand the retrieved context to Claude with a prompt that instructs it to answer only from the sources and to say so when it can't: from langchain_anthropic import ChatAnthropic; from langchain.chains import RetrievalQA; llm = ChatAnthropic(model='claude-sonnet-4-20250514', temperature=0); qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever); print(qa.invoke('What is our refund policy?')). Setting temperature to 0 and constraining the model to retrieved context is what turns a confident guesser into a grounded answerer. ## From tutorial to production This pipeline works, but a demo and a regulated production system are different animals. The gaps show up fast: how do you stop the model from answering when retrieval comes back weak? How do you keep one tenant's documents from leaking into another's answers? How do you log every exchange for audit, redact secrets, and prove the system behaved? That's the layer NeuralSeek adds on top of a RAG core — a minimum confidence floor, corpus isolation, prompt logging, and a built-in bake-off so you can pick the right model on production-representative results instead of vendor benchmarks. Build the pipeline above to understand the mechanics; reach for governance when it's time to ship. **Take it to production** - [Minimum Confidence %](https://neuralseek.ai/ai-grounded/minimum-confidence-percent) - [Corp Filter](https://neuralseek.ai/ai-grounded/corp-filter) - [Prompt Logging](https://neuralseek.ai/ai-grounded/prompt-logging) - [Built-in LLM bake-off](https://neuralseek.ai/ai-grounded/llm-bake-off) --- From NeuralSeek's AI Grounded — practical, web-verified guidance on building governed, grounded enterprise AI. NeuralSeek is the model-agnostic, governed AI platform you own: any LLM (swap with no rebuild), your data in your own tenant (cloud or on-prem), 118 guardrails enforced before any action, one container that runs anywhere.